FRLDM: Empowering K-nearest Neighbor (KNN) through FPGA-based Reduced-rank Local Distance Metric

While fast and accurate data classification techniques are vital to many applications, K-Nearest Neighbor algorithm (KNN) is considered the most important algorithm used in data mining, text categorization, and image recognition. However, conventional KNN for computing distances may not necessarily perform well for all problems. In this paper, we propose a new framework named FRLDM to empower KNN through FPGA-based reduced-rank local distance metric. Experimental results on the collection of classification problems and hardware measurement imply that the FRLDM offers notable performance advantages over other approaches on CPU.